{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val acc: 0.17  | lr: 2.193960255358841e-06 , weight decay: 5.105880230286675e-05\n",
      "val acc: 0.67  | lr: 0.005250900850478909 , weight decay: 3.6106609664678054e-08\n",
      "val acc: 0.06  | lr: 5.0846435194883996e-05 , weight decay: 3.082595058590657e-08\n",
      "val acc: 0.19  | lr: 2.9361907436786166e-05 , weight decay: 7.987799292352536e-05\n",
      "val acc: 0.64  | lr: 0.00401890898766125 , weight decay: 7.161948431560266e-05\n",
      "val acc: 0.08  | lr: 9.64025979933783e-06 , weight decay: 4.6498227396679375e-06\n",
      "val acc: 0.75  | lr: 0.0073414021299871 , weight decay: 9.691672433677264e-05\n",
      "val acc: 0.08  | lr: 0.00025565230430476697 , weight decay: 4.659825876291539e-05\n",
      "val acc: 0.18  | lr: 0.0012144055447711921 , weight decay: 2.291171904625234e-06\n",
      "val acc: 0.42  | lr: 0.005282372753289175 , weight decay: 8.770355486243856e-06\n",
      "val acc: 0.08  | lr: 0.0001235395068014494 , weight decay: 3.7786923742591535e-06\n",
      "val acc: 0.12  | lr: 0.00010332121932976084 , weight decay: 6.59996847062575e-08\n",
      "val acc: 0.15  | lr: 0.00102122334319266 , weight decay: 1.3748887570775167e-05\n",
      "val acc: 0.05  | lr: 1.339618042216334e-06 , weight decay: 2.68537922322901e-08\n",
      "val acc: 0.07  | lr: 1.5235048372898967e-05 , weight decay: 7.0312232780639235e-06\n",
      "val acc: 0.1  | lr: 1.0855331223431806e-06 , weight decay: 3.812338513762327e-08\n",
      "val acc: 0.07  | lr: 0.00020216461346566597 , weight decay: 3.588163846921526e-05\n",
      "val acc: 0.06  | lr: 1.642995169954241e-05 , weight decay: 3.1005300962658596e-07\n",
      "val acc: 0.12  | lr: 2.88402034478661e-05 , weight decay: 8.651103521750235e-05\n",
      "val acc: 0.16  | lr: 0.00011893851749443744 , weight decay: 3.139199821819926e-05\n",
      "val acc: 0.55  | lr: 0.005386996639947689 , weight decay: 5.14321977370273e-07\n",
      "val acc: 0.05  | lr: 1.6847316933685013e-06 , weight decay: 1.4806987895252358e-07\n",
      "val acc: 0.07  | lr: 4.7011026962516275e-06 , weight decay: 6.755338422953432e-07\n",
      "val acc: 0.08  | lr: 1.1835294735119652e-06 , weight decay: 7.540209665056899e-06\n",
      "val acc: 0.13  | lr: 1.4141770548279706e-06 , weight decay: 6.24449238339917e-07\n",
      "val acc: 0.18  | lr: 0.0006892289422239866 , weight decay: 4.55032611708575e-07\n",
      "val acc: 0.09  | lr: 2.7727244132659168e-05 , weight decay: 8.323372538753176e-05\n",
      "val acc: 0.12  | lr: 5.726776407275401e-05 , weight decay: 2.479134079242277e-06\n",
      "val acc: 0.14  | lr: 1.4785419655362269e-05 , weight decay: 2.4765543658820186e-08\n",
      "val acc: 0.12  | lr: 6.87230493752714e-06 , weight decay: 6.533886280349211e-08\n",
      "val acc: 0.04  | lr: 1.1196455706581022e-06 , weight decay: 5.513089016142606e-07\n",
      "val acc: 0.08  | lr: 1.9603583109292092e-05 , weight decay: 1.8207003538071795e-06\n",
      "val acc: 0.19  | lr: 0.000977745238329004 , weight decay: 1.6951841701807097e-07\n",
      "val acc: 0.1  | lr: 4.168403372757063e-06 , weight decay: 1.1245318892787182e-08\n",
      "val acc: 0.07  | lr: 5.717511831573686e-05 , weight decay: 5.35120768880581e-06\n",
      "val acc: 0.08  | lr: 2.4923174924310424e-05 , weight decay: 1.479316841177184e-07\n",
      "val acc: 0.12  | lr: 0.0008897116442707326 , weight decay: 4.050587663795256e-08\n",
      "val acc: 0.47  | lr: 0.0038407459027069006 , weight decay: 4.63124527611898e-06\n",
      "val acc: 0.51  | lr: 0.004786773191513566 , weight decay: 9.054897493022238e-06\n",
      "val acc: 0.17  | lr: 0.0012049555246857087 , weight decay: 3.068016098569243e-06\n",
      "val acc: 0.17  | lr: 8.251534558537598e-06 , weight decay: 7.0347399869005304e-06\n",
      "val acc: 0.24  | lr: 0.0016908974861535528 , weight decay: 1.5513358784642412e-06\n",
      "val acc: 0.3  | lr: 0.0036658763260994827 , weight decay: 4.878468245989768e-06\n",
      "val acc: 0.3  | lr: 0.0023007361298013397 , weight decay: 2.331657692838826e-08\n",
      "val acc: 0.28  | lr: 0.0019531927027446007 , weight decay: 1.8206045125779279e-06\n",
      "val acc: 0.15  | lr: 2.0748160530411765e-06 , weight decay: 4.267938251607762e-07\n",
      "val acc: 0.06  | lr: 1.006196698979746e-06 , weight decay: 2.0944202194034517e-05\n",
      "val acc: 0.64  | lr: 0.005614440815948914 , weight decay: 3.946870741053096e-06\n",
      "val acc: 0.13  | lr: 1.4818798491507965e-05 , weight decay: 5.270217500025105e-07\n",
      "val acc: 0.7  | lr: 0.00680486798691698 , weight decay: 3.7397076647278694e-05\n",
      "val acc: 0.12  | lr: 3.893898474152332e-06 , weight decay: 4.3611394606064783e-05\n",
      "val acc: 0.21  | lr: 0.002048320817266387 , weight decay: 6.343456042140722e-06\n",
      "val acc: 0.39  | lr: 0.0028446575628080714 , weight decay: 6.611575269394064e-07\n",
      "val acc: 0.1  | lr: 2.1517423396621397e-05 , weight decay: 6.577935616022381e-06\n",
      "val acc: 0.18  | lr: 0.001030337480968571 , weight decay: 9.799242088473687e-08\n",
      "val acc: 0.23  | lr: 0.00141502109992872 , weight decay: 1.3775138614365755e-05\n",
      "val acc: 0.55  | lr: 0.0025336918961036325 , weight decay: 3.472477571770625e-07\n",
      "val acc: 0.14  | lr: 6.251870670950292e-06 , weight decay: 4.254006569450843e-06\n",
      "val acc: 0.14  | lr: 4.635610229287579e-06 , weight decay: 1.067166577945372e-08\n",
      "val acc: 0.07  | lr: 7.367843826457877e-05 , weight decay: 7.943177308035566e-08\n",
      "val acc: 0.12  | lr: 3.9031365324916365e-06 , weight decay: 1.0103193021495626e-08\n",
      "val acc: 0.13  | lr: 0.0001258234349423731 , weight decay: 4.9737914582570405e-06\n",
      "val acc: 0.15  | lr: 1.1371953166140602e-05 , weight decay: 2.120112945531408e-07\n",
      "val acc: 0.09  | lr: 3.115421785252813e-05 , weight decay: 2.1474245893339375e-08\n",
      "val acc: 0.18  | lr: 0.00038243591541795697 , weight decay: 3.861823005156394e-06\n",
      "val acc: 0.13  | lr: 0.0005500004340454934 , weight decay: 3.934898723171701e-05\n",
      "val acc: 0.14  | lr: 0.001139448864779001 , weight decay: 2.606218220352921e-05\n",
      "val acc: 0.6  | lr: 0.004417330757368712 , weight decay: 2.1967801754090494e-07\n",
      "val acc: 0.15  | lr: 0.00016990531946192553 , weight decay: 2.9791837767424977e-07\n",
      "val acc: 0.12  | lr: 0.0008818729037736909 , weight decay: 8.13452621368053e-07\n",
      "val acc: 0.71  | lr: 0.008071363525720278 , weight decay: 1.5234302456620347e-07\n",
      "val acc: 0.15  | lr: 0.0008591828185189289 , weight decay: 5.842485989862336e-07\n",
      "val acc: 0.24  | lr: 0.0026108420910351806 , weight decay: 2.9034330282380085e-07\n",
      "val acc: 0.14  | lr: 2.807761755722818e-06 , weight decay: 7.089980185996024e-06\n",
      "val acc: 0.07  | lr: 1.8822367449299e-05 , weight decay: 3.294705270938927e-08\n",
      "val acc: 0.12  | lr: 5.50215287815403e-05 , weight decay: 3.380049101674527e-08\n",
      "val acc: 0.07  | lr: 8.17193482325916e-05 , weight decay: 9.014258954402298e-08\n",
      "val acc: 0.16  | lr: 0.0005267729062747294 , weight decay: 6.97901922993376e-08\n",
      "val acc: 0.05  | lr: 6.626669649096114e-06 , weight decay: 5.090729705266985e-05\n",
      "val acc: 0.1  | lr: 9.61639972419915e-05 , weight decay: 1.0606932569626338e-07\n",
      "val acc: 0.04  | lr: 2.7234129177042747e-05 , weight decay: 1.0963877250480176e-05\n",
      "val acc: 0.09  | lr: 1.5632216440330312e-06 , weight decay: 3.991117718412621e-06\n",
      "val acc: 0.11  | lr: 0.0002900851741386824 , weight decay: 6.801533650727991e-08\n",
      "val acc: 0.08  | lr: 3.1771682832836905e-06 , weight decay: 5.248270068637225e-07\n",
      "val acc: 0.13  | lr: 2.539243718993452e-05 , weight decay: 5.2175917191119975e-08\n",
      "val acc: 0.09  | lr: 0.00010347519550192611 , weight decay: 1.9632789489999374e-05\n",
      "val acc: 0.08  | lr: 1.693473022063604e-06 , weight decay: 3.7371587190865877e-07\n",
      "val acc: 0.13  | lr: 0.0004756777032268073 , weight decay: 1.811738363454981e-05\n",
      "val acc: 0.14  | lr: 0.0006915938420353594 , weight decay: 1.8229627841423338e-06\n",
      "val acc: 0.75  | lr: 0.008653373989462068 , weight decay: 2.309517419048648e-05\n",
      "val acc: 0.08  | lr: 6.397608602243154e-05 , weight decay: 9.458318048071252e-07\n",
      "val acc: 0.17  | lr: 1.090303067621998e-06 , weight decay: 1.1640154622058148e-06\n",
      "val acc: 0.05  | lr: 4.26690952075999e-05 , weight decay: 5.92289627845093e-07\n",
      "val acc: 0.08  | lr: 3.7763151882786592e-06 , weight decay: 3.130304607608978e-06\n",
      "val acc: 0.11  | lr: 3.623019271781082e-05 , weight decay: 1.111980616544922e-07\n",
      "val acc: 0.1  | lr: 3.304055522863677e-05 , weight decay: 2.205594148758429e-08\n",
      "val acc: 0.08  | lr: 0.0003679260448568293 , weight decay: 2.501664387673563e-05\n",
      "val acc: 0.69  | lr: 0.006507158479271918 , weight decay: 5.452838976110762e-06\n",
      "val acc: 0.1  | lr: 1.315462270997058e-06 , weight decay: 2.954022619543365e-08\n",
      "val acc: 0.07  | lr: 8.677284287504955e-06 , weight decay: 6.129729759850419e-08\n",
      "================Hyper-Parameter Optimization Result===================\n",
      "Best- 1 (val acc: 0.75 ) | lr:0.0073414021299871, weight decay:9.691672433677264e-05\n",
      "Best- 2 (val acc: 0.75 ) | lr:0.008653373989462068, weight decay:2.309517419048648e-05\n",
      "Best- 3 (val acc: 0.71 ) | lr:0.008071363525720278, weight decay:1.5234302456620347e-07\n",
      "Best- 4 (val acc: 0.7 ) | lr:0.00680486798691698, weight decay:3.7397076647278694e-05\n",
      "Best- 5 (val acc: 0.69 ) | lr:0.006507158479271918, weight decay:5.452838976110762e-06\n",
      "Best- 6 (val acc: 0.67 ) | lr:0.005250900850478909, weight decay:3.6106609664678054e-08\n",
      "Best- 7 (val acc: 0.64 ) | lr:0.00401890898766125, weight decay:7.161948431560266e-05\n",
      "Best- 8 (val acc: 0.64 ) | lr:0.005614440815948914, weight decay:3.946870741053096e-06\n",
      "Best- 9 (val acc: 0.6 ) | lr:0.004417330757368712, weight decay:2.1967801754090494e-07\n",
      "Best- 10 (val acc: 0.55 ) | lr:0.005386996639947689, weight decay:5.14321977370273e-07\n",
      "Best- 11 (val acc: 0.55 ) | lr:0.0025336918961036325, weight decay:3.472477571770625e-07\n",
      "Best- 12 (val acc: 0.51 ) | lr:0.004786773191513566, weight decay:9.054897493022238e-06\n",
      "Best- 13 (val acc: 0.47 ) | lr:0.0038407459027069006, weight decay:4.63124527611898e-06\n",
      "Best- 14 (val acc: 0.42 ) | lr:0.005282372753289175, weight decay:8.770355486243856e-06\n",
      "Best- 15 (val acc: 0.39 ) | lr:0.0028446575628080714, weight decay:6.611575269394064e-07\n",
      "Best- 16 (val acc: 0.3 ) | lr:0.0036658763260994827, weight decay:4.878468245989768e-06\n",
      "Best- 17 (val acc: 0.3 ) | lr:0.0023007361298013397, weight decay:2.331657692838826e-08\n",
      "Best- 18 (val acc: 0.28 ) | lr:0.0019531927027446007, weight decay:1.8206045125779279e-06\n",
      "Best- 19 (val acc: 0.24 ) | lr:0.0016908974861535528, weight decay:1.5513358784642412e-06\n",
      "Best- 20 (val acc: 0.24 ) | lr:0.0026108420910351806, weight decay:2.9034330282380085e-07\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 20 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 训练数据，验证数据，测试数据\n",
    "# train_data, validation_data, test_data\n",
    "\n",
    "# 从训练数据中分割20%作为验证数据\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "from common.util import shuffle_dataset\n",
    "from common.multi_layer_net import MultiLayerNet\n",
    "from common.trainer import Trainer\n",
    "\n",
    "# 0.读入MNIST数据\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True)\n",
    "\n",
    "# 为了实现高速化，减少训练数据\n",
    "x_train = x_train[:500]\n",
    "t_train = t_train[:500]\n",
    "\n",
    "# 1.打乱训练数据\n",
    "x_train, t_train = shuffle_dataset(x_train, t_train)\n",
    "\n",
    "# 2.分割验证数据\n",
    "validation_rate = 0.20\n",
    "validation_num = int(x_train.shape[0] * validation_rate)\n",
    "x_val = x_train[:validation_num]\n",
    "t_val = t_train[:validation_num]\n",
    "x_train = x_train[validation_num:]\n",
    "t_train = t_train[validation_num:]\n",
    "\n",
    "# 训练方法\n",
    "def __train(lr, weight_decay, epochs=50):\n",
    "    network = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100],\n",
    "                            output_size=10, weight_decay_lambda=weight_decay)\n",
    "    trainer = Trainer(network, x_train, t_train, x_val, t_val,\n",
    "                      epochs=epochs, mini_batch_size=100,\n",
    "                      optimizer='sgd', optimizer_param={'lr': lr}, verbose=False)\n",
    "    trainer.train()\n",
    "\n",
    "    return trainer.test_acc_list, trainer.train_acc_list\n",
    "\n",
    "# 超参数的随机搜索\n",
    "optimization_trial = 100\n",
    "results_val = {}\n",
    "results_train = {}\n",
    "for _ in range(optimization_trial):\n",
    "    # 指定搜索的超参数的范围================\n",
    "    weight_decay = 10 ** np.random.uniform(-8, -4)\n",
    "    lr = 10 ** np.random.uniform(-6, -2)\n",
    "    # =================================\n",
    "\n",
    "    val_acc_list, train_acc_list = __train(lr, weight_decay)\n",
    "    print(\"val acc:\", str(val_acc_list[-1]), \" | lr:\", str(lr), \", weight decay:\", str(weight_decay))\n",
    "    key = \"lr:\" + str(lr) + \", weight decay:\" + str(weight_decay)\n",
    "    results_val[key] = val_acc_list\n",
    "    results_train[key] = train_acc_list\n",
    "\n",
    "# 绘制图形\n",
    "print(\"================Hyper-Parameter Optimization Result===================\")\n",
    "graph_draw_num = 20\n",
    "col_num = 5\n",
    "row_num = int(graph_draw_num / col_num)\n",
    "i = 0\n",
    "\n",
    "for key, val_acc_list in sorted(results_val.items(), key=lambda x:x[1][-1], reverse=True):\n",
    "    print(\"Best-\", str(i+1), \"(val acc:\", str(val_acc_list[-1]), \") |\", key)\n",
    "\n",
    "    plt.subplot(row_num, col_num, i+1)\n",
    "    plt.title(\"Best-\" + str(i+1))\n",
    "    plt.ylim(0.0, 1.0)\n",
    "    if i%5: plt.yticks([])\n",
    "    plt.xticks([])\n",
    "    x = np.arange(len(val_acc_list))\n",
    "    plt.plot(x, val_acc_list)\n",
    "    plt.plot(x, results_train[key], \"--\")\n",
    "    i += 1\n",
    "\n",
    "    if i >= graph_draw_num:\n",
    "        break\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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